The invention relates to a method of inferring the state of a system and classifying data. The invention relates to an apparatus and a system for inferring the state of a system and visualizing the state. The invention relates to the computer program product for inferring the state of a system and visualizing the state.
Any arbitrary system of interest can have at least two states. The system of interest can be, for example, an apparatus, a human body, or a financial entity. Typically, the system of interest either functions correctly (normal state) or has a malfunction (error state). There may be several normal and/or error states. A good example is the healthiness of a human being: he or she can be healthy (normal state) or have a disease (error state), in which case the number of the error states may be large. The state of the system of interest defines which normal or error state the system of interest is in and how much the state of the system of interest differs from a control state specified beforehand. For example, in medical applications the state of the system of interest defines the disease a patient has and how far the disease has advanced, as compared with the normal, healthy state. In industrial applications the state of the system of interest defines the malfunction of an apparatus and how severe the malfunction is.
Computerized methods are needed in the above-mentioned analyses of the systems of interest to efficiently utilize multidimensional data and to find complex relations in the data. Each dimension of the data relates to an aspect of the particular system that is being measured (i.e. an indicator) and from which measurement values (indicator values) are gathered. Typically, the computerized methods give only a classification (normal/error) as an output. However, in many applications the computerized methods cannot make the final decision because of possible erroneous measurements and uncertainty in the data, or merely because the computer cannot fully mimic the knowledge and experience of an expert. In such cases, a human user needs to make the final decision.
Nowadays, there is typically a large number of data available to the user interpreting the state of a system of interest. For example, different signals and images measured and results of various tests may be available for the user to inspect. Some of these values and pieces of information may have additional information on the normal range of the value, and the user needs to observe this range in addition to the value itself. The different values and data may be at least partially conflicting, and the data may be heterogeneous so that combining the data heuristically or numerically may be difficult and unreliable. Determining the state of the system may therefore be very time-consuming and prone to errors in interpretation.
There is, therefore, a need for solutions that make it faster, easier and less prone to errors to infer a state of a system from heterogeneous information.